{"id":"W1999735728","doi":"10.1080/10962247.2014.921255","title":"Evaluating the capabilities of Aerosol-to-Liquid Particle Extraction System (ALPXS)/ICP-MS for monitoring trace metals in indoor air","year":2014,"lang":"en","type":"article","venue":"Journal of the Air & Waste Management Association","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"Health Canada; Clean Air Regulatory Agenda","keywords":"Aerosol; Inductively coupled plasma mass spectrometry; Particle (ecology); Sampling (signal processing); Extraction (chemistry); Volumetric flow rate; Environmental science; Particulates; Detection limit; Particle size; Mass spectrometry; Analytical Chemistry (journal); Chemistry; Environmental chemistry; Filter (signal processing); Chromatography; Computer science; Mechanics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008357386,0.0001008282,0.0002365308,0.00004336327,0.0002181945,0.0000252692,0.0002987701,0.00005257483,0.000009242973],"category_scores_gemma":[0.0006147166,0.00006630107,0.000131801,0.0002619561,0.00002480635,0.0004012746,0.0001026132,0.0001618691,0.000008406212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126348,"about_ca_system_score_gemma":0.00001312511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001318225,"about_ca_topic_score_gemma":0.00006202715,"domain_scores_codex":[0.9974263,0.000613621,0.0007583165,0.0001201545,0.0008040544,0.0002775148],"domain_scores_gemma":[0.9979323,0.0005186993,0.001236014,0.0002020546,0.00005262969,0.00005830324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001182867,0.000423403,0.1546889,0.001182224,0.0003424475,0.000001068915,0.01730508,0.7536926,0.03723913,0.001519024,0.00300867,0.0294145],"study_design_scores_gemma":[0.003922554,0.002640282,0.7965961,0.001215575,0.000598686,0.000006622678,0.03856172,0.05833907,0.09391421,0.001096874,0.002664741,0.0004435514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889325,0.00003076282,0.001644977,0.007765027,0.0005613579,0.0006969941,0.000002068035,0.000008048082,0.0003581979],"genre_scores_gemma":[0.9976298,0.000009166405,0.001328399,0.0002498533,0.0001884081,0.00003191311,2.001457e-7,0.000009951419,0.0005523599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6953536,"threshold_uncertainty_score":0.3303957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05241021529263835,"score_gpt":0.3474387902232385,"score_spread":0.2950285749306001,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}